Trend coupling analysis method for monitoring multiple information of ground pressure activity

Abstract

The application discloses a method for monitoring multi-element information trend coupling analysis of ground pressure activity, which comprises the following steps: acquiring real-time monitoring data of ground pressure, monitoring data integration, standardization, judging the trend size of monitoring data section based on time sequence change rate, analyzing multi-element monitoring data trend coupling value based on quadrant coordinate, and coupling data trend discrimination analysis. First, stress, displacement and acoustic emission multi-element monitoring data of the same field ground pressure activity are synchronously acquired. The original data are processed by time sequence linear interpolation, which is used for coupling trend discrimination. The data trend is calculated based on the time sequence change rate of the data section, the trend size of the monitoring data section is discriminated and analyzed, the coupling data trend coordinates are obtained by combining the trend discrimination indexes Ti obtained by two different monitoring data elements, and the ground pressure danger level is preliminarily discriminated. The application can improve the accuracy of judging the danger level of ground pressure activity to a certain extent by comprehensively combining various indexes and coupling analysis of the indexes.

Description

Technical Field

[0001] This invention relates to the field of mining safety technology, specifically a method for trend coupling analysis of multi-source information in ground pressure activity monitoring. Background Technology

[0002] Frequent ground pressure activity in mines is a problem faced by most deep-well mines. The high stress concentration caused by deep-well mining can easily lead to accidents such as roof collapse and roadway collapse, and can also induce rock bursts, which seriously threaten mine production and personnel safety.

[0003] The main methods for monitoring ground pressure in mines include acoustic emission monitoring, displacement monitoring, stress monitoring, and microseismic monitoring. Data analysis methods often rely on the experience of engineers and technicians based on single monitoring results, or on setting early warning thresholds through multi-parameter analysis. However, rock deformation and failure is a complex nonlinear process, releasing a great deal of information throughout the entire process from bearing capacity to instability. It is difficult to comprehensively reflect the stress and strain distribution and failure status of goafs and roadways using only a single monitoring technology and analysis. Therefore, its stability evaluation also has significant limitations and biases, specifically manifested in the following ways:

[0004] (1) Single monitoring method

[0005] Currently, stress monitoring or displacement monitoring is mostly used in ground pressure activity monitoring. Using a single monitoring method can only analyze and explain the changes in rock mass stress and displacement during the mining process. However, neither method can independently determine whether the rock mass is in a state of failure, let alone make accurate early warnings of rock mass failure and classify the warning levels.

[0006] (2) The prediction results are one-sided

[0007] Analyzing mine stress monitoring results to predict potential ground pressure manifestations is too simplistic. This is because stress reflects changes in intrinsic stress caused by mining disturbances. Stress increases and decreases can represent stress accumulation and dissipation, or the stages before and after peak failure. Therefore, using stress increases and decreases to judge rock mass instability and predict ground pressure manifestations is clearly too simplistic. Similarly, judging rock mass instability based on displacement changes is too arbitrary. Displacement monitoring measures external displacement caused by stress within the rock mass. These displacement changes may occur during the elastoplastic stage, where the rock mass displacement shows a linear relationship. The rock mass has not yet entered the yielding stage and has not truly become unstable. Therefore, relying solely on displacement changes to judge the stability of the rock mass is difficult to draw accurate conclusions, leading to deviations in the final prediction results.

[0008] (3) Lack of effective coupled prediction models

[0009] Currently, both methods are used in conjunction with the monitoring of ground pressure activity in large goaf areas of mines. However, the fusion analysis of monitoring results relies on threshold warnings. Because the changes in displacement and stress are diverse, there is still a lack of effective judgment criteria on how to analyze different forms of change and how to determine the danger level of both under different changing conditions. Therefore, the current coupled prediction method has significant limitations and cannot truly achieve accurate prediction of mine ground pressure. Summary of the Invention

[0010] The purpose of this invention is to provide a method for coupled analysis of multivariate information trends in ground pressure activity monitoring, so as to solve the problems mentioned in the background art.

[0011] To achieve the above objectives, the present invention provides the following technical solution: a multi-source information trend coupling analysis method for ground pressure activity monitoring, including acquiring real-time ground pressure monitoring data, data integrity and standardization, determining the trend magnitude of monitoring data segments based on the time-series rate of change, analyzing the coupling numerical values ​​of multi-source monitoring data trends based on quadrant coordinates, and performing trend discrimination analysis of the coupling data. The coupling analysis method is as follows:

[0012] S1. First, multi-dimensional monitoring data of stress, displacement, and acoustic emission of syn-domain ground pressure activity are acquired synchronously; S2. The initial data are processed by time-series linear interpolation to determine coupling trends;

[0013] S3. Calculate data trends by segmenting them based on the time-series change rate of data segments, and analyze the magnitude of the trend in the monitored data segments.

[0014] S4. The trend discrimination index Ti obtained from two different monitoring data elements is combined to obtain the coupled data trend coordinates. Based on the quadrant coordinate analysis, the position of the coupled trend coordinate points is analyzed to preliminarily determine the ground pressure hazard level.

[0015] S5. Finally, a combination of multiple indicators, including the coupled trend indicator C_Ti, the trend change rate R, and the data change amount, is used to perform coupled analysis to determine the hazard level of ground pressure activity.

[0016] Preferably, step S1 further includes the following steps:

[0017] S11. Displacement-acoustic emission co-source sensors are arranged in the roof and sidewall rock mass of the panel roadway to collect displacement data and cumulative acoustic emission energy. Stress-acoustic emission co-source sensors are arranged in the pillars of the panel and the pillars between the mining areas to collect stress data and cumulative acoustic emission energy. The sensors are connected to a multi-source data acquisition base station to realize continuous monitoring and record monitoring data. The monitoring base station uploads the data to the surface to obtain the monitoring data.

[0018] Preferably, step S2 further includes the following steps:

[0019] S21. The multi-source data acquisition base station in the mine collects data at certain time intervals. Based on the monitoring data of a certain time period, it is necessary to determine whether the original sequence is complete. In order to avoid the situation of missing data, we need to interpolate the original sequence to ensure the integrity of the data, standardize the acquired sequence set, and transform the data of the time period that needs to be coupled for data processing into data with a standard deviation of 1 and a mean of 0.

[0020] S22. Based on the above, the data sequence standardization formula is obtained:

[0021] y i =(x i -μ) / σ

[0022] Where x i σ represents the original data; μ represents the mean of the original data segment; σ represents the standard deviation of the original data segment.

[0023] Preferably, step S3 further includes the following steps:

[0024] S31. When determining the trend of monitored data, if the trend of the entire data segment needs to be determined, the data must first be segmented to obtain the trend magnitude of each segment, and then segmented according to the formula:

[0025] Where n is the total number of data points, and w is the number of segments (even number);

[0026] The trend discriminant index Ti is calculated using the trend discriminant formula, and the trend direction of the data is determined based on the magnitude of Ti.

[0027]

[0028] Where Nummax is the maximum value of the data sequence segment; Nummin is the minimum value of the data sequence segment; indexmax is the relative position of the maximum value in the data sequence segment; and indexmin is the relative position of the minimum value in the data sequence segment.

[0029] S32. When the maximum value in the segmented data is after the minimum value, indicating that the data sequence segment is in an upward trend, the calculated value according to the formula is a positive number. The larger the trend indicator Ti, the faster the upward trend of the data segment; the smaller the trend indicator Ti, the slower the upward trend of the data segment. Conversely, when the maximum value in the segmented data is before the minimum value, indicating that the data sequence segment is in a downward trend, the calculated value according to the formula is a negative number. The smaller the trend indicator Ti, the faster the downward trend of the data segment; the larger the trend indicator Ti, the slower the downward trend of the data segment. When the maximum value and the minimum value are equal, the calculated value according to the formula is 0, indicating that the data sequence segment is in a horizontal trend.

[0030] Preferably, step S4 further includes the following steps:

[0031] S41. The coupled trend index is a coordinate (xi, yi) composed of trend discrimination indices Ti derived from two different data elements. For example, (D_Ti, S_Ti) represents a coordinate point composed of stress-displacement trend indices, (Ae_Ti, S_Ti) represents a coordinate point composed of stress-acoustic emission trend indices, and (D_Ti, Ae_Ti) represents a coordinate point composed of displacement-acoustic emission trend indices. The degree of ground pressure hazard is determined based on which quadrant the coordinate point is in. Here, D_Ti is the stress trend index, S_Ti is the displacement trend index, and Ae_Ti is the acoustic emission trend index.

[0032] Preferably, step S5 further includes the following steps:

[0033] S51. Comparison of coupling trend magnitudes:

[0034] The coordinate points obtained in step S41 are in different quadrants. When the coordinates have negative values, the coupling trend index C_Ti is negative. The coupling trend index C_Ti is calculated and judged according to the formula. This value is the coordinate formed by the trend index of two sets of data. Then the distance from the coordinate point to the origin is calculated.

[0035] From the above, the coupling trend index C_Ti is obtained:

[0036] A positive value for the coupling trend index C_Ti indicates a higher degree of ground pressure hazard; a negative value indicates a lower degree of ground pressure hazard. Conversely, a positive value indicates a lower degree of ground pressure hazard; and a negative value indicates a higher degree of ground pressure hazard.

[0037] S52. Determine whether displacement data and acoustic emission data are increasing at an accelerated rate:

[0038] The magnitude of the trend change is obtained by subtracting two adjacent values ​​of the trend indicator, which is the trend change rate R.

[0039] From the above, the rate of change of coupling trend is:

[0040] R = Ti j -Ti j-1

[0041] The higher the value of the trend change rate, the higher the degree of ground pressure hazard. A trend change rate R of 0 indicates that the data is steadily increasing and the ground pressure safety level is relatively safe. If the trend change rate R is greater than 0, it indicates that the data is increasing at a rate of Ti, and further discriminant analysis is needed to determine the ground pressure safety level.

[0042] S53. Determine the amount of data change:

[0043] The change in data is obtained by subtracting the minimum value from the maximum value of each data segment. The larger the change, the higher the degree of ground pressure hazard. The priority for data change is: first, determine the change in displacement data, then consider the change in acoustic emission data.

[0044] S54. Comprehensive assessment of ground pressure safety level:

[0045] When the ground pressure level information cannot be determined from the data in the trend coupling mode, it is necessary to compare the magnitude of the coupling trend, the value of the rate of change of the coupling trend, and the magnitude of the change.

[0046] Preferably, step S54 further includes the following step:

[0047] S541. Principle for judging data size: If data anomalies occur for three consecutive days, for example, if the trend change rate R is greater than 0 and is greater than 0 for three consecutive days, then the ground pressure level is judged to be dangerous.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] 1. By using a homogeneous sensor to collect multi-source information from the same location, this invention solves the problem that a single device cannot collect multiple types of information and improves monitoring flexibility.

[0050] 2. This invention determines the trend of a single data point by analyzing its time-series change rate, and then performs coupled analysis of multiple data trends to dynamically predict the associated information trend of rock fracture. This solves the problem that multiple data points cannot be judged at the same scale, and can reduce the probability of prediction deviations.

[0051] 3. This invention improves the accuracy of ground pressure level determination through trend discrimination and comprehensive analysis of multiple data indicators. Attached Figure Description

[0052] Figure 1 This is an overall flowchart of the present invention;

[0053] Figure 2 This is the stress-displacement data trend coupling prediction graph of the present invention;

[0054] Figure 3 This is the stress-acoustic emission data trend coupling prediction graph of the present invention;

[0055] Figure 4 This is the displacement-acoustic emission data trend coupling prediction diagram of the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0058] Please see Figure 1 This invention provides an embodiment of a multi-source information trend coupling analysis method for ground pressure activity monitoring, which involves acquiring real-time ground pressure monitoring data, completing and standardizing the monitoring data, determining the trend magnitude of monitoring data segments based on the time-series rate of change, analyzing the coupling numerical values ​​of multi-source monitoring data trends based on quadrant coordinates, and performing trend discrimination analysis of the coupling data. The coupling analysis method is characterized by the following:

[0059] S1. First, multi-dimensional monitoring data of stress, displacement, and acoustic emission of syn-domain ground pressure activity are acquired synchronously; S2. The initial data are processed by time-series linear interpolation to determine coupling trends;

[0060] S3. Calculate data trends by segmenting them based on the time-series change rate of data segments, and analyze the magnitude of the trend in the monitored data segments.

[0061] S4. The trend discrimination index Ti obtained from two different monitoring data elements is combined to obtain the coupled data trend coordinates. Based on the quadrant coordinate analysis, the position of the coupled trend coordinate points is analyzed to preliminarily determine the ground pressure hazard level.

[0062] S5. Finally, a combination of multiple indicators, including the coupled trend indicator C_Ti, the trend change rate R, and the data change amount, is used to perform coupled analysis to determine the hazard level of ground pressure activity.

[0063] Step S1 also includes the following steps:

[0064] S11. Displacement-acoustic emission co-source sensors are arranged in the roof and sidewall rock mass of the panel roadway to collect displacement data and cumulative acoustic emission energy. Stress-acoustic emission co-source sensors are arranged in the pillars of the panel and the pillars between the mining areas to collect stress data and cumulative acoustic emission energy. The sensors are connected to a multi-source data acquisition base station to realize continuous monitoring and record monitoring data. The monitoring base station uploads the data to the surface to obtain the monitoring data.

[0065] Step S2 also includes the following steps:

[0066] S21. The multi-source data acquisition base station in the mine collects data at certain time intervals. Based on the monitoring data for a certain time period, it is necessary to determine whether the original sequence is complete. To avoid gaps in the collected data, we need to interpolate the original sequence to ensure data integrity. The acquired sequence set is standardized, transforming the data for the time period requiring coupled data processing into data with a standard deviation of 1 and a mean of 0.

[0067] S22. Based on the above, the data sequence standardization formula is obtained:

[0068] y i =(x i -μ) / σ

[0069] Where x i σ represents the original data; μ represents the mean of the original data segment; σ represents the standard deviation of the original data segment.

[0070] Step S3 also includes the following steps:

[0071] S31. When determining the trend of monitored data, if the trend of the entire data segment needs to be determined, the data must first be segmented to obtain the trend magnitude of each segment, and then segmented according to the formula:

[0072] Where n is the total number of data points, and w is the number of segments (even number);

[0073] The trend discriminant index Ti is calculated using the trend discriminant formula, and the trend direction of the data is determined based on the magnitude of Ti.

[0074]

[0075] Where Nummax is the maximum value of the data sequence segment; Nummin is the minimum value of the data sequence segment; indexmax is the relative position of the maximum value in the data sequence segment; and indexmin is the relative position of the minimum value in the data sequence segment.

[0076] S32. When the maximum value in the segmented data is after the minimum value, indicating that the data sequence segment is in an upward trend, the calculated value according to the formula is a positive number. The larger the trend indicator Ti, the faster the upward trend of the data segment; the smaller the trend indicator Ti, the slower the upward trend of the data segment. Conversely, when the maximum value in the segmented data is before the minimum value, indicating that the data sequence segment is in a downward trend, the calculated value according to the formula is a negative number. The smaller the trend indicator Ti, the faster the downward trend of the data segment; the larger the trend indicator Ti, the slower the downward trend of the data segment. When the maximum value and the minimum value are equal, the calculated value according to the formula is 0, indicating that the data sequence segment is in a horizontal trend.

[0077] Step S4 also includes the following steps:

[0078] S41. The coupled trend index is a coordinate (xi, yi) composed of trend discrimination indices Ti derived from two different data elements. For example, (D_Ti, S_Ti) represents a coordinate point composed of stress-displacement trend indices, (Ae_Ti, S_Ti) represents a coordinate point composed of stress-acoustic emission trend indices, and (D_Ti, Ae_Ti) represents a coordinate point composed of displacement-acoustic emission trend indices. The degree of ground pressure hazard is determined based on which quadrant the coordinate point is in. Here, D_Ti is the stress trend index, S_Ti is the displacement trend index, and Ae_Ti is the acoustic emission trend index.

[0079] Step S5 also includes the following steps:

[0080] S51. Comparison of coupling trend magnitudes:

[0081] The coordinate points obtained in step S41 are in different quadrants. When the coordinates have negative values, the coupling trend index C_Ti is negative. The coupling trend index C_Ti is calculated and judged according to the formula. This value is the coordinate formed by the trend index of two sets of data. Then the distance from the coordinate point to the origin is calculated.

[0082] From the above, the coupling trend index C_Ti is obtained:

[0083] A positive value for the coupling trend index C_Ti indicates a higher degree of ground pressure hazard; a negative value indicates a lower degree of ground pressure hazard. Conversely, a positive value indicates a lower degree of ground pressure hazard; and a negative value indicates a higher degree of ground pressure hazard.

[0084] S52. Determine whether displacement data and acoustic emission data are increasing at an accelerated rate:

[0085] The magnitude of the trend change is obtained by subtracting two adjacent values ​​of the trend indicator, which is the trend change rate R.

[0086] From the above, the rate of change of coupling trend is:

[0087] R = Ti j -Ti j-1

[0088] The higher the value of the trend change rate, the higher the degree of ground pressure hazard. A trend change rate R of 0 indicates that the data is steadily increasing and the ground pressure safety level is relatively safe. If the trend change rate R is greater than 0, it indicates that the data is increasing at a rate of Ti, and further discriminant analysis is needed to determine the ground pressure safety level.

[0089] S53. Determine the amount of data change:

[0090] The change in data is obtained by subtracting the minimum value from the maximum value of each data segment. The larger the change, the higher the degree of ground pressure hazard. The priority for data change is to first determine the change in displacement data, and then consider the change in acoustic emission data.

[0091] S54. Comprehensive assessment of ground pressure safety level:

[0092] When the ground pressure level information cannot be determined by the data in the trend coupling mode, it is necessary to compare the magnitude of the coupling trend, the value of the coupling trend change rate, and the magnitude of the change. The principle for judging the magnitude of the data is: if there are data anomalies for three consecutive days, for example, if the trend change rate R is greater than 0 and is greater than 0 for three consecutive days, then the ground pressure level is judged to be dangerous.

[0093] As attached Figure 2 As shown, the multi-source information trend coupling analysis method for ground pressure activity monitoring includes a displacement-stress data trend coupling mode:

[0094] Based on the quadrant, the ground pressure hazard levels can be classified as follows:

[0095] I. The data trend coupling coordinate point is at the origin, the stress data trend index S_Ti is 0, and the displacement data trend index D_Ti is 0, indicating that the stress data and displacement data have not changed, and the stress trend and displacement trend are stable. Under this condition, there will be no ground pressure manifestation, and the ground pressure activity hazard level is safe.

[0096] II. The coordinate point is located in the lower half of the y-axis. The stress data trend index S_Ti is less than 0, and the displacement data trend index D_Ti is 0. The stress data shows a downward trend, while the displacement data is constant. This indicates that the stress data is decreasing, which means that the stress on the rock is decreasing and there is no displacement deviation. Under this condition, there will be no ground pressure manifestation, and the ground pressure activity hazard level is safe.

[0097] Point Ⅲ is located in the upper half of the y-axis. The stress data trend index S_Ti is greater than 0, and the displacement trend index D_Ti is 0. The stress data shows an upward trend, while the displacement data is constant. This indicates that the stress data is increasing with the S_Ti index, which means that the stress value of the rock is increasing and there is no displacement deviation. The risk level of ground pressure activity is relatively safe.

[0098] Point IV, located in the first quadrant, shows both stress trend index S_Ti and displacement trend index D_Ti greater than 0, indicating an increasing trend in both data. This suggests stress concentration at the monitored location, and deformation of the internal rock structure under stress, causing displacement of the roof or rock wall. Under these conditions, no ground pressure will manifest, and the ground pressure activity hazard level is relatively safe. However, as the stress trend index S_Ti and displacement trend index D_Ti continue to increase, it is necessary to determine whether the trend indices are increasing rapidly. If the trend indices increase rapidly, the ground pressure activity hazard level rises to relatively dangerous.

[0099] V is located at the coordinate point in the fourth quadrant. The stress data trend index S_Ti is less than 0, and the displacement data trend index D_Ti is greater than 0, indicating that the stress data is decreasing and the displacement data is increasing. Under this condition, ground pressure will occur, and the ground pressure activity hazard level is dangerous.

[0100] As attached Figure 3 As shown, the multi-source information trend coupling analysis method for ground pressure activity monitoring includes a stress-acoustic emission data trend coupling mode:

[0101] Based on the quadrant, the ground pressure hazard levels can be classified as follows:

[0102] I. The data trend coupling coordinate point is at the origin, the stress data trend index S_Ti is 0, and the acoustic emission data trend index Ae_Ti is 0, indicating that the stress data and the cumulative acoustic emission energy per unit time have not changed, and the stress and acoustic emission data trends are stable. Under this condition, there will be no ground pressure manifestation, and the ground pressure activity hazard level is safe.

[0103] II. The coordinate point is located in the lower half of the y-axis. The stress data trend index S_Ti is less than 0, and the acoustic emission data trend index Ae_Ti is 0, indicating that the stress data is decreasing and the cumulative acoustic emission energy per unit time has not changed. Under this condition, there will be no ground pressure manifestation, and the ground pressure activity hazard level is safe.

[0104] Point Ⅲ is located in the second quadrant and the negative half of the x-axis. The stress data trend index S_Ti is greater than 0, and the acoustic emission data trend index Ae_Ti is less than 0, indicating that the stress data is on an upward trend and the cumulative acoustic emission energy per unit time is on a downward trend. This indicates that the stress on the rock is increasing, but no more associated information is generated, indicating that the rock has not fractured. Under this condition, there will be no ground pressure manifestation, and the ground pressure activity hazard level is safe.

[0105] Point IV is located in the third quadrant. The stress data trend index S_Ti is less than 0, and the acoustic emission data trend index Ae_Ti is less than 0. This indicates that the stress data and the cumulative acoustic emission energy per unit time are decreasing. The rock pressure is decreasing, and the associated information is decreasing. Under this condition, there will be no ground pressure manifestation, and the ground pressure activity hazard level is safe.

[0106] Point V, located in the upper half of the y-axis, shows a stress data trend index S_Ti greater than 0 and an acoustic emission data trend index Ae_Ti of 0. This indicates an upward trend in stress data, while the cumulative acoustic emission energy per unit time remains unchanged. Under these conditions, the monitored area is experiencing increased pressure, the rock is in the initial stage of bearing, and rock cracks are unlikely to develop. The risk level of ground pressure activity is considered relatively safe.

[0107] Point V, located in the first quadrant, shows both the stress data trend index S_Ti and the acoustic emission data trend index Ae_Ti greater than 0. This indicates an upward trend in both stress and cumulative acoustic emission energy, suggesting stress concentration at the monitored location and the appearance of new cracks within the rock mass. Under these conditions, the rock is in a stable bearing phase and is unlikely to experience large-scale damage in the short term. The ground pressure activity hazard level is considered relatively dangerous.

[0108] VI is located at the coordinate point in the fourth quadrant. The stress data trend index S_Ti is less than 0, and the acoustic emission data trend index Ae_Ti is greater than 0, indicating that the stress data is decreasing and the cumulative acoustic emission energy is increasing. This indicates that the rock at the monitoring location is unloading and the rock mass is fracturing. A large ground pressure will appear in the short term, and the ground pressure activity hazard level is dangerous.

[0109] As attached Figure 4 As shown, the multi-source information trend coupling analysis method for ground pressure activity monitoring includes a displacement-acoustic emission data trend coupling mode:

[0110] Ground pressure hazard levels can be classified according to quadrants:

[0111] Ⅰ The data trend coupling coordinate point is at the origin, the displacement data trend index D_Ti is 0, and the acoustic emission data trend index Ae_Ti is 0, indicating that the displacement data and the cumulative acoustic emission energy per unit time have not changed, and the displacement and acoustic emission data trends are stable. Under this condition, there will be no ground pressure manifestation, and the ground pressure activity hazard level is safe.

[0112] II. The coordinate point is located in the lower half of the y-axis. The acoustic emission data trend index Ae_Ti is less than 0, and the displacement data trend index D_Ti is 0. This indicates that the cumulative acoustic emission energy per unit time is decreasing and the displacement data is stable. Under this condition, there will be no ground pressure manifestation, and the ground pressure activity hazard level is safe.

[0113] Point Ⅲ is located in the upper half of the y-axis. The acoustic emission data trend index Ae_Ti is greater than 0, and the displacement data trend index D_Ti is 0. This indicates that the cumulative acoustic emission energy per unit time is increasing, the displacement data is stable, cracks appear inside the rock mass, but there is no macroscopic ground pressure. Under this condition, the ground pressure activity hazard level is relatively safe.

[0114] Point IV is located in the first quadrant. The acoustic emission data trend index Ae_Ti is greater than 0, and the displacement data trend index D_Ti is greater than 0, indicating that both the cumulative acoustic emission energy and displacement data are on the rise. The number of internal cracks in the rock mass is increasing, and macroscopic ground pressure is present but not obvious. At this time, the ground pressure activity hazard level is relatively dangerous. However, as the acoustic emission data trend index and displacement data trend index continue to increase, further discriminant analysis is needed to determine the ground pressure activity hazard level.

[0115] Working principle: Before performing coupled analysis on the multi-source monitoring information of ground pressure, displacement data and cumulative acoustic emission energy are first collected using displacement-acoustic emission co-source sensors to obtain real-time monitoring data. To avoid gaps in the collected data, interpolation is performed on the original data series to ensure data integrity. The obtained sequence set is then standardized, and the data for the time period to be coupled and processed is transformed into data with a standard deviation of 1 and a mean of 0. Next, the trend discrimination index Ti is calculated using a trend discrimination formula. Based on the magnitude of Ti, the trend direction of the data is determined. Finally, the ground pressure risk level is determined based on different trend directions.

[0116] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0117] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for multivariate information trend coupling analysis of ground pressure activity monitoring, including acquiring real-time ground pressure monitoring data, data integrity and standardization, determining the trend magnitude of monitoring data segments based on time-series rate of change, analyzing the coupled numerical values ​​of multivariate monitoring data trends based on quadrant coordinates, and performing coupled data trend discriminant analysis, characterized in that... The coupling analysis method is as follows: S1. First, multi-dimensional monitoring data on stress, displacement, and acoustic emission of syn-domain ground pressure activity are acquired simultaneously; S2. The raw data is processed by time-series linear interpolation to determine the coupling trend; S3. Calculate data trends by segmenting them based on the time-series change rate of data segments, and analyze the magnitude of the trend in the monitored data segments. Step S3 further includes the following steps: S31. When determining the trend of monitoring data, if the trend of the entire data segment is to be determined, the data must first be segmented to obtain the trend of each segment. The trend discriminant index Ti is calculated using the trend discriminant formula, and the trend direction of the data is determined based on the magnitude of Ti. Where Nummax is the maximum value of the data sequence segment; Nummin is the minimum value of the data sequence segment; indexmax is the relative position of the maximum value in the data sequence segment; and indexmin is the relative position of the minimum value in the data sequence segment. S32. When the maximum value in the segmented data is after the minimum value, that is, the data sequence segment is in an upward trend, the value is calculated according to the formula. The result is a positive number. The larger the trend index Ti is, the faster the upward trend of the data segment is. The smaller the trend index Ti is, the slower the upward trend of the data segment is. Conversely, when the maximum value in the segmented data is before the minimum value, that is, the data sequence segment is in a downward trend, the result calculated according to the formula is a negative number. The smaller the trend index Ti is, the more rapid the downward trend of the data segment is; the larger the trend index Ti is, the more gradual the downward trend of the data segment is. When the maximum value is equal to the minimum value, the value is calculated according to the formula, and the result is 0, which means that the data sequence segment has a horizontal trend. S4. The trend discrimination index Ti obtained from two different monitoring data elements is combined to obtain the coupled trend coordinates. Based on the quadrant coordinate analysis, the position of the coupled trend coordinate points is analyzed to preliminarily determine the ground pressure hazard level. Step S4 further includes the following steps: S41. The coupled trend indicator is a coordinate (x) formed by combining trend discriminant indicators Ti derived from two different data elements. i y i ); S5. Finally, a combination of multiple indicators, namely the coupled trend indicator C_Ti, the trend change rate R, and the data change amount, is used to perform coupled analysis to determine the hazard level of ground pressure activity. Step S5 further includes the following steps: S51. Comparison of coupling trend magnitudes: The coordinate points obtained in step S41 are in different quadrants. When the coordinates have negative values, the coupling trend index C_Ti is negative. The coupling trend index C_Ti is calculated and judged according to the formula. This value is the coordinate formed by the trend index of two sets of data. Then the distance from the coordinate point to the origin is calculated. The coupling trend index C_Ti is obtained; The coupling trend index C_Ti is positive. The larger the value of the coupling trend index, the higher the degree of ground pressure hazard. The result is negative. The smaller the value of the coupling trend index, the higher the degree of ground pressure hazard. Conversely, if the result is positive, the smaller the value of the coupling trend index, the lower the degree of ground pressure hazard. The result is negative; the larger the value of the coupling trend index, the lower the degree of ground pressure hazard. S52. Determine whether displacement data and acoustic emission data are increasing at an accelerated rate: The magnitude of the trend change is obtained by subtracting two adjacent values ​​of the trend indicator, which is the trend change rate R. From the above, the rate of change of coupling trend is: The larger the value of the trend change rate, the higher the degree of ground pressure hazard. A trend change rate R of 0 indicates that the data is growing steadily and the ground pressure safety level is relatively safe. If the trend change rate R is greater than 0, it indicates that the data is growing at a rate of Ti, and further discriminant analysis is needed to determine the ground pressure safety level. S53. Determine the amount of data change: The change in data is obtained by subtracting the minimum value from the maximum value of each data segment. The greater the change, the higher the degree of ground pressure hazard. The priority of the data change is: first judge the change in displacement data, then look at the change in acoustic emission data. S54. Comprehensive assessment of ground pressure safety level: The ground pressure safety level is determined by comprehensively considering the magnitude of the coupling trend, the rate of change of the coupling trend, and the magnitude of the change.

2. The multi-source information trend coupling analysis method for monitoring ground pressure activity according to claim 1, characterized in that: Step S1 further includes the following steps: S11. Displacement-acoustic emission co-source sensors are arranged in the roof and sidewall rock mass of the panel roadway to collect displacement data and cumulative acoustic emission energy. Stress-acoustic emission co-source sensors are arranged in the pillars of the panel and the pillars between the mining areas to collect stress data and cumulative acoustic emission energy. The sensors are connected to a multi-source data acquisition base station to realize continuous monitoring and record monitoring data. The monitoring base station uploads the data to the surface to obtain the monitoring data.

3. The multi-source information trend coupling analysis method for monitoring ground pressure activity according to claim 1, characterized in that: Step S2 further includes the following steps: S21. The multi-source data acquisition base station in the mine collects data at certain time intervals. Based on the monitoring data of a certain time period, it is necessary to determine whether the original sequence is complete, interpolate the original data series to ensure the integrity of the data, standardize the acquired sequence set, and transform the data of the time period that needs to be coupled for data processing into data with a standard deviation of 1 and a mean of 0. S22. Based on the above, the data sequence standardization formula is obtained: in This is the original data; This is the average value of the original data segment; This represents the standard deviation of the original data segment.

4. The multi-source information trend coupling analysis method for monitoring ground pressure activity according to claim 1, characterized in that: Step S54 further includes the following steps: S541. Principle for judging data size: If abnormal data occurs for three consecutive days, the ground pressure level is judged to be dangerous.

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